Intelligent Inventory and Supply Chain Management Agent

Agent Description:

The Intelligent Inventory and Supply Chain Management Agent automates the entire inventory replenishment process - from analysis to procurement. It proactively identifies low-stock items, forecasts future demand, and calculates optimal order quantities. It also evaluates and ranks suppliers based on performance metrics to recommend the most suitable vendor. This end-to-end automation ensures consistent stock availability and cost-effective sourcing.

Purpose and Components
  • Purpose: To automate inventory management by proactively identifying low-stock items, forecasting demand, calculating optimal order quantities, and recommending the best-performing suppliers to ensure stock availability and cost-effectiveness.
  • Components:

    • Inventory Analyzer: An agent to query inventory levels and identify items needing replenishment.

    • Demand Forecaster: An agent to predict future demand and calculate necessary order quantities.

    • Supplier Recommender: An agent to evaluate and rank suppliers based on performance metrics.

    • SQL Database Toolkit: Tools used by multiple agents to interact with different SQLite databases (Inventory Performance, Supplier Performance).

Supported Capabilities

  • Querying an InventoryPerformance SQL table for product stock levels, demand, reorder points, and supplier details.

  • Identifying items that are low stock based on two criteria:

    • CurrentStock < ReorderPoint

    • (CurrentStock / MonthlyDemand) < 0.5 (less than 15 days of stock)

  • Generating a JSON summary of low-stock items.

  • Forecasting future demand using a weighted average of the last 3 months' demand.

  • Calculating the recommended order quantity based on forecasted demand, lead time, safety stock, and current stock.

  • Querying a SupplierPerformance SQL table for supplier metrics (timeliness, quality, cost, responsiveness, audits, incidents).

  • Calculating a composite performance score for suppliers based on weighted metrics.

  • Ranking suppliers based on their performance scores for specific products identified as low stock.

  • Recommending the best supplier for each low-stock item based on calculated scores and providing a rationale.

  • Outputting results (low stock items, replenishment plan, supplier selection) in structured JSON and human-readable formats.

LLM Used

  • GPT_4O_MINI

    Note: To learn more about the LLM and to modify its behavior, refer to the Configuring LLM settings section.

Sub-Agents

1. Inventory Data Analyzer

  • Role: Inventory analyzer

  • Scope: Retrieve and summarize product-level inventory and sales records from the SQL database.

  • Description: This agent starts by querying the InventoryPerformance SQL table. It identifies items that are either below their reorder point or have less than 15 days of stock remaining based on monthly demand. It then generates a structured JSON list summarizing these specific low-stock items, including their current stock, reorder point, lead time, and current supplier.

  • LLM Used: Default (Inherits from parent agent).

  • Tools Used: SQL - Toolkit (InventoryPerformance DB)

2. Demand Forecaster and Optimizer

  • Role: Demand forecaster

  • Scope: Use the output of Subagent 1 to forecast demand and determine optimal reorder quantities.

  • Description: This agent receives the list of low-stock items. For each item, it forecasts future demand using a weighted average of the past three months' demand (data assumed available or retrieved implicitly). It then calculates the RecommendedOrder quantity needed, considering lead time, safety stock, and current stock. The output is a replenishment plan in JSON format.

  • LLM Used: Default (Inherits from parent agent).

  • Tools Used: None

3. Supplier Negotiation and Procurement Adjuster

  • Role: Supplier recommender

  • Scope: Connect to supplier SQL dataset to evaluate vendor performance and suggest purchase allocation.

  • Description: This final agent takes the replenishment plan. For the suppliers associated with the low-stock items, it queries the SupplierPerformance SQL database to retrieve performance metrics. It calculates a weighted composite PerformanceScore for each relevant supplier, ranks them, and recommends the best supplier for each item needing replenishment. The output is a human-readable report detailing the product, best supplier, score, and rationale.

  • LLM Used: Default (Inherits from parent agent).

  • Tools Used: SQL - Toolkit (SupplierPerformance DB)

Tools Used:

  • SQL - Toolkit (SQLite) – Used by:

    • Inventory Data Analyzer to query the InventoryPerformance table.

    • Supplier Negotiation and Procurement Adjuster to query the SupplierPerformance table.

Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary

  1. The Inventory Data Analyzer (start node) queries the inventory database, identifies low-stock items based on reorder points or days-of-stock remaining, and outputs a JSON list of these items.

  2. This list is passed to the Demand Forecaster & Optimizer.

  3. The Forecaster calculates the ForecastedDemand (using weighted historical data) and the RecommendedOrder quantity for each low-stock item, outputting a JSON replenishment plan.

  4. This plan is passed to the Supplier Negotiation & Procurement Adjuster (end node).

  5. The Adjuster queries the supplier database for performance data related to the suppliers of the low-stock items.

  6. It calculates a PerformanceScore for each relevant supplier, ranks them, and outputs a human-readable recommendation of the best supplier for each item needing an order, along with the rationale.

Sample Questions:

  • Identify all low-stock items and generate a replenishment plan.

  • Forecast demand for items below reorder point and calculate order quantities.